Most teams we work with want to say their product is AI-powered. Few have asked whether their organisation is ready to support that claim. We have observed a recurring pattern where a team commits to building a machine learning system and, after months, discovers that their data isn't clean enough to train on. The infrastructure then fails to reliably serve a model. In such cases, our diagnosis has often concluded that the foundation is the problem. Machine learning readiness is a precondition, and one must begin with a straightforward question - "Do we have the data pipeline, infrastructure and cross-functional alignment to make this sustainable beyond a PoC?" That question is what prompted our first article titled "The Machine Learning Readiness Checklist". We cover everything a team needs, from data and infrastructure to the right kind of team members. All of this must be done before committing serious engineering time and budgets. If you're a founder, engineering lead or a decision maker within your organisation who wants to build things correctly, we encourage you to read the full piece here:

https://www.algorithmic.co/blogs/the-machine-learning-checklist We'll be sharing more each week. Follow along if this is relevant to what you're building.

P.S. This article is from late 2025 but more than ever relevant.

Love the vibe of this!

INTERSTELLAR BY AI 🪐 A tribute to the film that started it all for me. 🎥✨

I've been producing AI video for a while now, but this one hits different. Interstellar is the gold standard for scale and emotion, and I wanted to see how far I could push the current tools to respect that legacy.

From the dusty car interior to the crushing silence of Gargantua—this is AI filmmaking with a human soul.

Interstellar is the perfect film to tribute with AI — the scale, the silence, the emotion. This hits really different!